Photochemical reactions are fundamental in many biological (e.g., photosynthesis and vision) and technological (e.g., solar cells and light emitting diode (LED) displays) settings. Such reactions, as well as many spectroscopic measurements, involve electronic excited states of molecules and their concomitant structural changes. The reactions and associated dynamics are energetically subtle and require highly accurate descriptions of the relevant molecular forces. It is highly desirable in the art to be able to simulate and predict results for these and other types of reactions. One exemplary benefit of simulating and predicting reactions is to limit the amount of real-world experimental reactions needed. For example, this may avoid useless chemical combinations and may provide new combinations not previously considered. However, conventional simulation and prediction methods have been inadequate for several reasons.
Reliable methods for prediction are computationally very expensive even for small molecular reactions, and rapidly approach the impossible for reactions in complex environments, such as in solvents (e.g., water), in solid cages (e.g., zeolites), or with more complex molecules, such as proteins (e.g., protein ion channels). Hence, having very fast semiempirical potentials that accurately reproduce higher-level quantum chemistry results would make it possible to address critical biological processes and technologically useful chemical reactions, or dramatically reduce searches for potentially technologically useful light-activated reactions.
Established semiempirical quantum chemistry methods, known by acronyms such as MNDO, AM1, and PM3 with well-established parameter databases, and software, such as MOPAC, MOLCAS, and MOLPRO, have had parameter sets hand-designed and optimized to predict ground-state energies—not excited state energies. For ethylene, for example, AM1 or PM3 parameter sets incorrectly obtain a pyramidalized structure as the lowest-energy excited state. Thus, the carefully established parameter sets yield inaccurate potential energy surfaces and unphysical reaction dynamics. Further, previous reoptimization attempts to improve excited-state potential energy surfaces have met with limited success.